#1: UNDERSTANDING THE PROBLEM STATEMENT & USE CASE

Problem Statement: Predicting the used car prices by discovering the features which affect the price.

Use Case: This Project can be used by car dealers or companies like CARS24 & SPINNY to predict used car prices and understand the key factors involved in the same.

#2: IMPORTING & INSTALLING REQUIRED LIBRARIES/DATASETS

!pip install xgboost
!pip install plotly

Requirement already satisfied: xgboost in ./opt/anaconda3/lib/python3.9/site-packages (1.6.1)
Requirement already satisfied: scipy in ./opt/anaconda3/lib/python3.9/site-packages (from xgboost) (1.7.3)
Requirement already satisfied: numpy in ./opt/anaconda3/lib/python3.9/site-packages (from xgboost) (1.21.5)
Requirement already satisfied: plotly in ./opt/anaconda3/lib/python3.9/site-packages (5.6.0)
Requirement already satisfied: six in ./opt/anaconda3/lib/python3.9/site-packages (from plotly) (1.16.0)
Requirement already satisfied: tenacity>=6.2.0 in ./opt/anaconda3/lib/python3.9/site-packages (from plotly) (8.0.1)

import numpy as np                                                          # Using mutli-dimensional array objects                                                                
import pandas as pd                                                         # Data Manipulation
import seaborn as sns                                                       # Data Visualization
import matplotlib.pyplot as plt                                             # Data Visualization 
import plotly.express as px                                                 # Interacative Data Visualization 
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot  # Offline version of the Plotly modules
import warnings                                                             # Ignoring the warnings 
warnings.filterwarnings("ignore") 
from sklearn.model_selection import train_test_split                        # Machine Learning

df = pd.read_csv("cars_data.csv")

df.head(5)

	Make	Model	Type	Origin	DriveTrain	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
0	Acura	MDX	SUV	Asia	All	$36,945	$33,337	3.5	6.0	265	17	23	4451	106	189
1	Acura	RSX Type S 2dr	Sedan	Asia	Front	$23,820	$21,761	2.0	4.0	200	24	31	2778	101	172
2	Acura	TSX 4dr	Sedan	Asia	Front	$26,990	$24,647	2.4	4.0	200	22	29	3230	105	183
3	Acura	TL 4dr	Sedan	Asia	Front	$33,195	$30,299	3.2	6.0	270	20	28	3575	108	186
4	Acura	3.5 RL 4dr	Sedan	Asia	Front	$43,755	$39,014	3.5	6.0	225	18	24	3880	115	197

df.tail(5)

	Make	Model	Type	Origin	DriveTrain	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
423	Volvo	C70 LPT convertible 2dr	Sedan	Europe	Front	$40,565	$38,203	2.4	5.0	197	21	28	3450	105	186
424	Volvo	C70 HPT convertible 2dr	Sedan	Europe	Front	$42,565	$40,083	2.3	5.0	242	20	26	3450	105	186
425	Volvo	S80 T6 4dr	Sedan	Europe	Front	$45,210	$42,573	2.9	6.0	268	19	26	3653	110	190
426	Volvo	V40	Wagon	Europe	Front	$26,135	$24,641	1.9	4.0	170	22	29	2822	101	180
427	Volvo	XC70	Wagon	Europe	All	$35,145	$33,112	2.5	5.0	208	20	27	3823	109	186

#3: EDA

df.columns

Index(['Make', 'Model', 'Type', 'Origin', 'DriveTrain', 'MSRP', 'Invoice',
       'EngineSize', 'Cylinders', 'Horsepower', 'MPG_City', 'MPG_Highway',
       'Weight', 'Wheelbase', 'Length'],
      dtype='object')

df.shape

(428, 15)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 428 entries, 0 to 427
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Make         428 non-null    object 
 1   Model        428 non-null    object 
 2   Type         428 non-null    object 
 3   Origin       428 non-null    object 
 4   DriveTrain   428 non-null    object 
 5   MSRP         428 non-null    object 
 6   Invoice      428 non-null    object 
 7   EngineSize   428 non-null    float64
 8   Cylinders    426 non-null    float64
 9   Horsepower   428 non-null    int64  
 10  MPG_City     428 non-null    int64  
 11  MPG_Highway  428 non-null    int64  
 12  Weight       428 non-null    int64  
 13  Wheelbase    428 non-null    int64  
 14  Length       428 non-null    int64  
dtypes: float64(2), int64(6), object(7)
memory usage: 50.3+ KB

# Converting MSRP and Invoice to integer datatype by removing $ & (,)
df["MSRP"] = df["MSRP"].str.replace("$", "")
df["MSRP"] = df["MSRP"].str.replace(",", "")
df["MSRP"] = df["MSRP"].astype(int)
df["Invoice"] = df["Invoice"].str.replace("$", "")
df["Invoice"] = df["Invoice"].str.replace(",", "")
df["Invoice"] = df["Invoice"].astype(int)

df.head()

	Make	Model	Type	Origin	DriveTrain	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
0	Acura	MDX	SUV	Asia	All	36945	33337	3.5	6.0	265	17	23	4451	106	189
1	Acura	RSX Type S 2dr	Sedan	Asia	Front	23820	21761	2.0	4.0	200	24	31	2778	101	172
2	Acura	TSX 4dr	Sedan	Asia	Front	26990	24647	2.4	4.0	200	22	29	3230	105	183
3	Acura	TL 4dr	Sedan	Asia	Front	33195	30299	3.2	6.0	270	20	28	3575	108	186
4	Acura	3.5 RL 4dr	Sedan	Asia	Front	43755	39014	3.5	6.0	225	18	24	3880	115	197

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 428 entries, 0 to 427
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Make         428 non-null    object 
 1   Model        428 non-null    object 
 2   Type         428 non-null    object 
 3   Origin       428 non-null    object 
 4   DriveTrain   428 non-null    object 
 5   MSRP         428 non-null    int64  
 6   Invoice      428 non-null    int64  
 7   EngineSize   428 non-null    float64
 8   Cylinders    426 non-null    float64
 9   Horsepower   428 non-null    int64  
 10  MPG_City     428 non-null    int64  
 11  MPG_Highway  428 non-null    int64  
 12  Weight       428 non-null    int64  
 13  Wheelbase    428 non-null    int64  
 14  Length       428 non-null    int64  
dtypes: float64(2), int64(8), object(5)
memory usage: 50.3+ KB

round(df.describe(),2)

	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
count	428.00	428.00	428.00	426.00	428.00	428.00	428.00	428.00	428.00	428.00
mean	32774.86	30014.70	3.20	5.81	215.89	20.06	26.84	3577.95	108.15	186.36
std	19431.72	17642.12	1.11	1.56	71.84	5.24	5.74	758.98	8.31	14.36
min	10280.00	9875.00	1.30	3.00	73.00	10.00	12.00	1850.00	89.00	143.00
25%	20334.25	18866.00	2.38	4.00	165.00	17.00	24.00	3104.00	103.00	178.00
50%	27635.00	25294.50	3.00	6.00	210.00	19.00	26.00	3474.50	107.00	187.00
75%	39205.00	35710.25	3.90	6.00	255.00	21.25	29.00	3977.75	112.00	194.00
max	192465.00	173560.00	8.30	12.00	500.00	60.00	66.00	7190.00	144.00	238.00

#checking duplicates
df.duplicated().sum()

0

#checking no. of null values in each column
df.isnull().sum()

Make           0
Model          0
Type           0
Origin         0
DriveTrain     0
MSRP           0
Invoice        0
EngineSize     0
Cylinders      2
Horsepower     0
MPG_City       0
MPG_Highway    0
Weight         0
Wheelbase      0
Length         0
dtype: int64

#checking % of null values in each column
df.isnull().sum() * 100 / len(df)

Make           0.00000
Model          0.00000
Type           0.00000
Origin         0.00000
DriveTrain     0.00000
MSRP           0.00000
Invoice        0.00000
EngineSize     0.00000
Cylinders      0.46729
Horsepower     0.00000
MPG_City       0.00000
MPG_Highway    0.00000
Weight         0.00000
Wheelbase      0.00000
Length         0.00000
dtype: float64

#handling null values by dropping them since they are extremely less
df = df.dropna()

df.isnull().sum()

Make           0
Model          0
Type           0
Origin         0
DriveTrain     0
MSRP           0
Invoice        0
EngineSize     0
Cylinders      0
Horsepower     0
MPG_City       0
MPG_Highway    0
Weight         0
Wheelbase      0
Length         0
dtype: int64

#4.1: Univariate Analysis

df.Make.unique()

array(['Acura', 'Audi', 'BMW', 'Buick', 'Cadillac', 'Chevrolet',
       'Chrysler', 'Dodge', 'Ford', 'GMC', 'Honda', 'Hummer', 'Hyundai',
       'Infiniti', 'Isuzu', 'Jaguar', 'Jeep', 'Kia', 'Land Rover',
       'Lexus', 'Lincoln', 'MINI', 'Mazda', 'Mercedes-Benz', 'Mercury',
       'Mitsubishi', 'Nissan', 'Oldsmobile', 'Pontiac', 'Porsche', 'Saab',
       'Saturn', 'Scion', 'Subaru', 'Suzuki', 'Toyota', 'Volkswagen',
       'Volvo'], dtype=object)

fig = px.histogram(df, x = "Make",labels = {"Make":"Manufacturer"},title = "MAKE OF THE CAR",color_discrete_sequence = ["blue"])
fig.show()

df.Type.unique()

array(['SUV', 'Sedan', 'Sports', 'Wagon', 'Truck', 'Hybrid'], dtype=object)

fig = px.histogram(df, x = "Type",labels = {"Type":"Type"},title = "TYPE OF THE CAR",color_discrete_sequence = ["green"])           
fig.show()

df.Origin.unique()

array(['Asia', 'Europe', 'USA'], dtype=object)

fig = px.histogram(df, x = "Origin",labels = {"Origin":"Origin"},title = "LOCATION OF THE CAR SALES",color_discrete_sequence = ["orange"])     
fig.show()

df.DriveTrain.unique()

array(['All', 'Front', 'Rear'], dtype=object)

fig = px.histogram(df, x = "DriveTrain",labels = {"DriveTrain":"Drivetrain"},title = "DRIVE TRAIN OF THE CAR",color_discrete_sequence = ["red"])
fig.show()

fig = px.violin(df, x = "EngineSize",labels = {"EngineSize":"EngineSize"},title = "ENGINE SIZE OF THE CARS",color_discrete_sequence = ["purple"])
fig.show()

fig = px.violin(df, x = "Cylinders",labels = {"Cylinders":"Cylinders"},title = "CYLINDERS OF THE CARS",color_discrete_sequence = ["blue"])
fig.show()

fig = px.violin(df, x = "Horsepower",labels = {"Horsepower":"Horsepower"},title = "HORSEPOWER OF THE CARS",color_discrete_sequence = ["green"])
fig.show()

fig = px.violin(df, x = "MPG_City",labels = {"MPG_City":"MPG_City"},title = "MPG in City OF THE CARS",color_discrete_sequence = ["yellow"])
fig.show()

fig = px.violin(df, x = "MPG_Highway",labels = {"MPG_Highway":"MPG_Highway"},title = "MPG IN HIGHWAY OF THE CARS",color_discrete_sequence = ["orange"])
fig.show()

fig = px.violin(df, x = "Weight",labels = {"Weight":"Weight"},title = "WEIGHT OF THE CAR",color_discrete_sequence = ["red"])
fig.show()

fig = px.violin(df, x = "Length",labels = {"Length":"Length"},title ="LENGTH OF THE CARS",color_discrete_sequence = ["purple"])
fig.show()

fig = px.box(df, x = "MSRP",labels = {"MSRP":"MSRP"},title ="MSRP OF THE CARS",color_discrete_sequence = ["blue"])
fig.show()

fig = px.box(df, x = "Invoice",labels = {"Invoice":"Invoice"},title ="INVOICE OF THE CARS",color_discrete_sequence = ["green"])
fig.show()

Univariate Analysis:

1. There are 38 car manufactures in which Toyota has maximum cars while Hummer has least.
2. There are 6 Types of Cars in which Sedan type is maximum(more than the cumulative sum of other types) while Hybrid type is the least.
3. The 3 Origins of all cars: ASIA, EUROPE & USA.
4. As expected Mileage Per Gallon of cars in Highways is more than that of Cities.
5. The majority of MSRP lies in range of 10.28k-65k. There are certain outliers too.

4.2: Bivariate Analysis

fig = px.histogram(df, x = "Make",color = "Origin",labels = {"Make":"Manufacturer"},title = "MAKE OF THE CAR Vs LOCATION",)                
fig.show()

fig = px.histogram(df, x = "Make",color = "Type",labels = {"Make":"Manufacturer"},title = "MAKE OF THE CAR Vs TYPE")                  
fig.show()

# Plot the make of the car and its location
fig = px.histogram(df, x = "Type",color = "Origin",labels = {"Make":"Manufacturer"},title = "TYPE OF THE CAR Vs LOCATION")
fig.show()

# Plot the make of the car and its location
fig = px.histogram(df, x = "Type",color = "DriveTrain",labels = {"Make":"Manufacturer"},title = "TYPE OF THE CAR Vs DriveTrain")
fig.show()

# Plot the make of the car and its location
fig = px.histogram(df, x = "Origin",color = "DriveTrain",labels = {"Make":"Manufacturer"},title = "Location OF THE CAR Vs DriveTrain")
fig.show()

df_MSRP_Invoice=df[["MSRP","Invoice"]]
sns.boxplot(data=df_MSRP_Invoice)

<AxesSubplot:>

Bivariate Analysis:

1. Hybrid cars have been manufactured only in ASIA by Honda & Toyota.
2. Porsche manufactures most Sports cars(88% of all cars they manufacture).
3. There are no manufactures of Trucks in Europe.
4. The values of MSRP and Invoice are highly coorelated. Thus Invoice can be dropped before applying ML model.

# 4.3: Multivariate Analysis

sns.pairplot(data = df) 

<seaborn.axisgrid.PairGrid at 0x7f8f386fb730>

round(df.corr(),2)

	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
MSRP	1.00	1.00	0.57	0.65	0.83	-0.48	-0.44	0.45	0.15	0.17
Invoice	1.00	1.00	0.57	0.65	0.82	-0.47	-0.44	0.44	0.15	0.17
EngineSize	0.57	0.57	1.00	0.91	0.79	-0.72	-0.73	0.81	0.64	0.64
Cylinders	0.65	0.65	0.91	1.00	0.81	-0.68	-0.68	0.74	0.55	0.55
Horsepower	0.83	0.82	0.79	0.81	1.00	-0.68	-0.65	0.63	0.39	0.38
MPG_City	-0.48	-0.47	-0.72	-0.68	-0.68	1.00	0.94	-0.74	-0.51	-0.50
MPG_Highway	-0.44	-0.44	-0.73	-0.68	-0.65	0.94	1.00	-0.79	-0.53	-0.47
Weight	0.45	0.44	0.81	0.74	0.63	-0.74	-0.79	1.00	0.76	0.69
Wheelbase	0.15	0.15	0.64	0.55	0.39	-0.51	-0.53	0.76	1.00	0.89
Length	0.17	0.17	0.64	0.55	0.38	-0.50	-0.47	0.69	0.89	1.00

plt.figure(figsize = (8,8))
sns.heatmap(df.corr(), cmap="YlGnBu", annot = True)

<AxesSubplot:>

fig = px.scatter_3d(df, x='Make', y='Type', z='DriveTrain',color='Origin')
fig.show()

Multivariate Analysis: MSRP has highest positive coorelation with HORSEPOWER.

# 5: PREPARING THE DATA FOR MODEL TRAINING

df.head()

	Make	Model	Type	Origin	DriveTrain	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
0	Acura	MDX	SUV	Asia	All	36945	33337	3.5	6.0	265	17	23	4451	106	189
1	Acura	RSX Type S 2dr	Sedan	Asia	Front	23820	21761	2.0	4.0	200	24	31	2778	101	172
2	Acura	TSX 4dr	Sedan	Asia	Front	26990	24647	2.4	4.0	200	22	29	3230	105	183
3	Acura	TL 4dr	Sedan	Asia	Front	33195	30299	3.2	6.0	270	20	28	3575	108	186
4	Acura	3.5 RL 4dr	Sedan	Asia	Front	43755	39014	3.5	6.0	225	18	24	3880	115	197

# Performing One-Hot Encoding for "Make", "Model", "Type", "Origin", and "DriveTrain"
df_dum = pd.get_dummies(df, columns=["Make", "Model", "Type", "Origin", "DriveTrain"])

df_dum

	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length	...	Type_Sedan	Type_Sports	Type_Truck	Type_Wagon	Origin_Asia	Origin_Europe	Origin_USA	DriveTrain_All	DriveTrain_Front	DriveTrain_Rear
0	36945	33337	3.5	6.0	265	17	23	4451	106	189	...	0	0	0	0	1	0	0	1	0	0
1	23820	21761	2.0	4.0	200	24	31	2778	101	172	...	1	0	0	0	1	0	0	0	1	0
2	26990	24647	2.4	4.0	200	22	29	3230	105	183	...	1	0	0	0	1	0	0	0	1	0
3	33195	30299	3.2	6.0	270	20	28	3575	108	186	...	1	0	0	0	1	0	0	0	1	0
4	43755	39014	3.5	6.0	225	18	24	3880	115	197	...	1	0	0	0	1	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
423	40565	38203	2.4	5.0	197	21	28	3450	105	186	...	1	0	0	0	0	1	0	0	1	0
424	42565	40083	2.3	5.0	242	20	26	3450	105	186	...	1	0	0	0	0	1	0	0	1	0
425	45210	42573	2.9	6.0	268	19	26	3653	110	190	...	1	0	0	0	0	1	0	0	1	0
426	26135	24641	1.9	4.0	170	22	29	2822	101	180	...	0	0	0	1	0	1	0	0	1	0
427	35145	33112	2.5	5.0	208	20	27	3823	109	186	...	0	0	0	1	0	1	0	1	0	0

426 rows × 483 columns

df_data = df_dum.drop(["Invoice"], axis = 1)
# Invoice feature do not contribute to car price prediction 

df_data

	MSRP	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length	Make_Acura	...	Type_Sedan	Type_Sports	Type_Truck	Type_Wagon	Origin_Asia	Origin_Europe	Origin_USA	DriveTrain_All	DriveTrain_Front	DriveTrain_Rear
0	36945	3.5	6.0	265	17	23	4451	106	189	1	...	0	0	0	0	1	0	0	1	0	0
1	23820	2.0	4.0	200	24	31	2778	101	172	1	...	1	0	0	0	1	0	0	0	1	0
2	26990	2.4	4.0	200	22	29	3230	105	183	1	...	1	0	0	0	1	0	0	0	1	0
3	33195	3.2	6.0	270	20	28	3575	108	186	1	...	1	0	0	0	1	0	0	0	1	0
4	43755	3.5	6.0	225	18	24	3880	115	197	1	...	1	0	0	0	1	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
423	40565	2.4	5.0	197	21	28	3450	105	186	0	...	1	0	0	0	0	1	0	0	1	0
424	42565	2.3	5.0	242	20	26	3450	105	186	0	...	1	0	0	0	0	1	0	0	1	0
425	45210	2.9	6.0	268	19	26	3653	110	190	0	...	1	0	0	0	0	1	0	0	1	0
426	26135	1.9	4.0	170	22	29	2822	101	180	0	...	0	0	0	1	0	1	0	0	1	0
427	35145	2.5	5.0	208	20	27	3823	109	186	0	...	0	0	0	1	0	1	0	1	0	0

426 rows × 482 columns

# Feeding input features to X and output (MSRP) to y
X = df_data.drop("MSRP", axis = 1)
y = df_data["MSRP"]

X = np.array(X)

y = np.array(y)

X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2)

df_data.shape

(426, 482)

X_train.shape

(340, 481)

X_test.shape

(86, 481)

y_train.shape

(340,)

y_test.shape

(86,)

# 5: PREPARING THE DATA FOR MODEL TRAINING

df.head()

	Make	Model	Type	Origin	DriveTrain	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
0	Acura	MDX	SUV	Asia	All	36945	33337	3.5	6.0	265	17	23	4451	106	189
1	Acura	RSX Type S 2dr	Sedan	Asia	Front	23820	21761	2.0	4.0	200	24	31	2778	101	172
2	Acura	TSX 4dr	Sedan	Asia	Front	26990	24647	2.4	4.0	200	22	29	3230	105	183
3	Acura	TL 4dr	Sedan	Asia	Front	33195	30299	3.2	6.0	270	20	28	3575	108	186
4	Acura	3.5 RL 4dr	Sedan	Asia	Front	43755	39014	3.5	6.0	225	18	24	3880	115	197

# Performing One-Hot Encoding for "Make", "Model", "Type", "Origin", and "DriveTrain"
df_dum = pd.get_dummies(df, columns=["Make", "Model", "Type", "Origin", "DriveTrain"])

df_dum

	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length	...	Type_Sedan	Type_Sports	Type_Truck	Type_Wagon	Origin_Asia	Origin_Europe	Origin_USA	DriveTrain_All	DriveTrain_Front	DriveTrain_Rear
0	36945	33337	3.5	6.0	265	17	23	4451	106	189	...	0	0	0	0	1	0	0	1	0	0
1	23820	21761	2.0	4.0	200	24	31	2778	101	172	...	1	0	0	0	1	0	0	0	1	0
2	26990	24647	2.4	4.0	200	22	29	3230	105	183	...	1	0	0	0	1	0	0	0	1	0
3	33195	30299	3.2	6.0	270	20	28	3575	108	186	...	1	0	0	0	1	0	0	0	1	0
4	43755	39014	3.5	6.0	225	18	24	3880	115	197	...	1	0	0	0	1	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
423	40565	38203	2.4	5.0	197	21	28	3450	105	186	...	1	0	0	0	0	1	0	0	1	0
424	42565	40083	2.3	5.0	242	20	26	3450	105	186	...	1	0	0	0	0	1	0	0	1	0
425	45210	42573	2.9	6.0	268	19	26	3653	110	190	...	1	0	0	0	0	1	0	0	1	0
426	26135	24641	1.9	4.0	170	22	29	2822	101	180	...	0	0	0	1	0	1	0	0	1	0
427	35145	33112	2.5	5.0	208	20	27	3823	109	186	...	0	0	0	1	0	1	0	1	0	0

426 rows × 483 columns

df_data = df_dum.drop(["Invoice"], axis = 1)
# Invoice feature do not contribute to car price prediction 

df_data

	MSRP	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length	Make_Acura	...	Type_Sedan	Type_Sports	Type_Truck	Type_Wagon	Origin_Asia	Origin_Europe	Origin_USA	DriveTrain_All	DriveTrain_Front	DriveTrain_Rear
0	36945	3.5	6.0	265	17	23	4451	106	189	1	...	0	0	0	0	1	0	0	1	0	0
1	23820	2.0	4.0	200	24	31	2778	101	172	1	...	1	0	0	0	1	0	0	0	1	0
2	26990	2.4	4.0	200	22	29	3230	105	183	1	...	1	0	0	0	1	0	0	0	1	0
3	33195	3.2	6.0	270	20	28	3575	108	186	1	...	1	0	0	0	1	0	0	0	1	0
4	43755	3.5	6.0	225	18	24	3880	115	197	1	...	1	0	0	0	1	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
423	40565	2.4	5.0	197	21	28	3450	105	186	0	...	1	0	0	0	0	1	0	0	1	0
424	42565	2.3	5.0	242	20	26	3450	105	186	0	...	1	0	0	0	0	1	0	0	1	0
425	45210	2.9	6.0	268	19	26	3653	110	190	0	...	1	0	0	0	0	1	0	0	1	0
426	26135	1.9	4.0	170	22	29	2822	101	180	0	...	0	0	0	1	0	1	0	0	1	0
427	35145	2.5	5.0	208	20	27	3823	109	186	0	...	0	0	0	1	0	1	0	1	0	0

426 rows × 482 columns

# Feeding input features to X and output (MSRP) to y
X = df_data.drop("MSRP", axis = 1)
y = df_data["MSRP"]

X = np.array(X)

y = np.array(y)

X_train, X_test, y_train, y_test= train_test_split(X, y, test_size = 0.2)

df_data.shape

(426, 482)

X_train.shape

(340, 481)

X_test.shape

(86, 481)

y_train.shape

(340,)

y_test.shape

(86,)

# 6.1: TRAINING AND EVALUATING MULTIPLE LINEAR REGRESSION

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, accuracy_score
from math import sqrt

LinearRegression_model= LinearRegression()
LinearRegression_model.fit(X_train, y_train)

LinearRegression()

accuracy_LinearRegression = LinearRegression_model.score(X_test, y_test)
accuracy_LinearRegression

0.8432973844392416

print("ACCURACY of Multiple Linear Regression: ",round(accuracy_LinearRegression*100,2),"%")

ACCURACY of Multiple Linear Regression:  84.33 %

# 6.2: TRAINING AND EVALUATING DECISION TREE

from sklearn.tree import DecisionTreeRegressor

DecisionTree_model = DecisionTreeRegressor()
DecisionTree_model.fit(X_train, y_train)

DecisionTreeRegressor()

accuracy_DecisionTree = DecisionTree_model.score(X_test, y_test)
accuracy_DecisionTree

0.8825769736325962

print("ACCURACY of Decision Tree:",round(accuracy_DecisionTree*100,2),"%")

ACCURACY of Decision Tree: 88.26 %

# 6.3: TRAINING AND EVALUATING RANDOM FOREST REGRESSION

from sklearn.ensemble import RandomForestRegressor

RandomForest_model = RandomForestRegressor(n_estimators= 5, max_depth= 5)
RandomForest_model.fit(X_train, y_train)

RandomForestRegressor(max_depth=5, n_estimators=5)

accuracy_RandomForest= RandomForest_model.score(X_test, y_test)
accuracy_RandomForest

0.8431022604395081

print("ACCURACY of Random Forest Regression:",round(accuracy_RandomForest*100,2),"%")

ACCURACY of Random Forest Regression: 84.31 %

# 6.4: TRAINING AND EVALUATING XGBOOST ALGORITHM

from xgboost import XGBRegressor

model = XGBRegressor()
model.fit(X_train, y_train)

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
             importance_type=None, interaction_constraints='',
             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, ...)

accuracy_XGBoost = model.score(X_test, y_test)
accuracy_XGBoost

0.9368475564239244

print("ACCURACY of XGBOOST Algorithm:",round(accuracy_XGBoost*100,2),"%")

ACCURACY of XGBOOST Algorithm: 93.68 %

# 7: COMPARING MODELS AND CALCULATING REGRESSION KPIs

y_predict_linear = LinearRegression_model.predict(X_test)

fig = sns.regplot(y_predict_linear, y_test, color = 'red', marker = "^")
fig.set(title = "Linear Regression Model", xlabel = "Predicted Price of the used cars ($)", ylabel = "Actual Price of the used cars ($)")

[Text(0.5, 1.0, 'Linear Regression Model'),
 Text(0.5, 0, 'Predicted Price of the used cars ($)'),
 Text(0, 0.5, 'Actual Price of the used cars ($)')]

RMSE= float(format(np.sqrt(mean_squared_error(y_test, y_predict_linear)), ".3f"))
MSE= mean_squared_error(y_test, y_predict_linear)
MAE= mean_absolute_error(y_test, y_predict_linear)
r2= r2_score(y_test, y_predict_linear)

print('RMSE =',RMSE, '\nMSE =',MSE, '\nMAE =',MAE, '\nR2 =', r2) 

RMSE = 6976.547 
MSE = 48672211.62598064 
MAE = 5060.856301243534 
R2 = 0.8432973844392416

y_predict_RandomForest = RandomForest_model.predict(X_test)

fig = sns.regplot(y_predict_RandomForest, y_test, color = 'blue', marker = "s")
fig.set(title = "Random Forest Regression Model", xlabel = "Predicted Price of the used cars ($)", ylabel= "Actual Price of the used cars ($)")

[Text(0.5, 1.0, 'Random Forest Regression Model'),
 Text(0.5, 0, 'Predicted Price of the used cars ($)'),
 Text(0, 0.5, 'Actual Price of the used cars ($)')]

RMSE= float(format(np.sqrt(mean_squared_error(y_test, y_predict_RandomForest)), ".3f"))
MSE= mean_squared_error(y_test, y_predict_RandomForest)
MAE= mean_absolute_error(y_test, y_predict_RandomForest)
r2= r2_score(y_test, y_predict_RandomForest)

print('RMSE =',RMSE, '\nMSE =',MSE, '\nMAE =',MAE, '\nR2 =', r2) 

RMSE = 6980.889 
MSE = 48732817.6125135 
MAE = 4217.669264831376 
R2 = 0.8431022604395081

y_predict_XGBoost = model.predict(X_test)

fig = sns.regplot(y_predict_XGBoost, y_test, color = 'green', marker = "D")
fig.set(title = "XGBoost Model", xlabel = "Predicted Price of the used cars ($)", ylabel = "Actual Price of the used cars ($)")

[Text(0.5, 1.0, 'XGBoost Model'),
 Text(0.5, 0, 'Predicted Price of the used cars ($)'),
 Text(0, 0.5, 'Actual Price of the used cars ($)')]

RMSE = float(format(np.sqrt(mean_squared_error(y_test, y_predict_XGBoost)), ".3f"))
MSE = mean_squared_error(y_test, y_predict_XGBoost)
MAE = mean_absolute_error(y_test, y_predict_XGBoost)
r2 = r2_score(y_test, y_predict_XGBoost)

print('RMSE =',RMSE, '\nMSE =',MSE, '\nMAE =',MAE, '\nR2 =', r2) 

RMSE = 4428.917 
MSE = 19615301.80867185 
MAE = 3039.6424305050873 
R2 = 0.9368475564239244

# 8: CONCLUSION

XGBoost model scores 94% accuracy which clearly outperforms the Multiple Linear Regression(84%), Decision Tree(88%) and Random Forest Regression(84%) models.